setting baseline experiments with data format

LeQua2022/TODO.txt

@@ -0,0 +1,7 @@
+1. los test hay que hacerlos suponiendo que las etiquetas no existen, es decir, viendo los resultados en los ficheros "prevalences" (renominar)
+2. tablas?
+3. fetch dataset (download, unzip, etc.)
+4. model selection
+5. plots
+6. estoy leyendo los samples en orden, y no hace falta. Sería mejor una función genérica que lee todos los ejemplos y
+    que de todos modos genera un output con el mismo nombre del file

LeQua2022/data.py

@@ -1,16 +1,27 @@
 import quapy as qp
 import numpy as np
+import sklearn
 
 
-def load_binary_raw_document(path):
-    documents, labels = qp.data.from_text(path, verbose=0, class2int=True)
-    labels = np.asarray(labels)
-    labels[np.logical_or(labels == 1, labels == 2)] = 0
-    labels[np.logical_or(labels == 4, labels == 5)] = 1
-    return documents, labels
+# def load_binary_raw_document(path):
+#     documents, labels = qp.data.from_text(path, verbose=0, class2int=True)
+#     labels = np.asarray(labels)
+#     labels[np.logical_or(labels == 1, labels == 2)] = 0
+#     labels[np.logical_or(labels == 4, labels == 5)] = 1
+#     return documents, labels
 
 
 def load_multiclass_raw_document(path):
     return qp.data.from_text(path, verbose=0, class2int=False)
 
 
+def load_binary_vectors(path, nF=None):
+    return sklearn.datasets.load_svmlight_file(path, n_features=nF)
+
+
+if __name__ == '__main__':
+    X, y = load_binary_vectors('./data/T1A/public/training_vectors.txt')
+    print(X.shape)
+    print(y)
+
+

LeQua2022/main_binary_raw.py

@@ -8,16 +8,16 @@ from tqdm import tqdm
 import quapy as qp
 from quapy.data import LabelledCollection
 from quapy.method.aggregative import *
-from data import load_binary_raw_document
+from data import load_binary_vectors
 import os
 
-path_binary_raw = 'binary_raw'
-result_path = os.path.join('results', 'binary_raw')
+path_binary_vector = './data/T1A'
+result_path = os.path.join('results', 'T1A')  # binary - vector
 os.makedirs(result_path, exist_ok=True)
 
-train_file = os.path.join(path_binary_raw, 'documents', 'training.txt')
+train_file = os.path.join(path_binary_vector, 'public', 'training_vectors.txt')
 
-train = LabelledCollection.load(train_file, load_binary_raw_document)
+train = LabelledCollection.load(train_file, load_binary_vectors)
 
 print(train.classes_)
 print(len(train))
@@ -36,7 +36,7 @@ for quantifier in [CC, ACC, PCC, PACC, EMQ, HDy]:
     for sample_set, sample_size in [('validation', 1000)]:#, ('test', 5000)]:
         ae_errors, rae_errors = [], []
         for i in tqdm(range(sample_size), total=sample_size, desc=f'testing {quantifier_name} in {sample_set}'):
-            test_file = os.path.join(path_binary_raw, 'documents', f'{sample_set}_{i}.txt')
+            test_file = os.path.join(path_binary_vector, 'documents', f'{sample_set}_{i}.txt')
             test = LabelledCollection.load(test_file, load_binary_raw_document, classes=train.classes_)
             test.instances = tfidf.transform(test.instances)
             qp.environ['SAMPLE_SIZE'] = len(test)

LeQua2022/main_binary_vector.py

@@ -0,0 +1,92 @@
+import pickle
+
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from tqdm import tqdm
+import pandas as pd
+
+import quapy as qp
+from quapy.data import LabelledCollection
+from quapy.method.aggregative import *
+import quapy.functional as F
+from data import load_binary_vectors
+import os
+
+path_binary_vector = './data/T1A'
+result_path = os.path.join('results', 'T1A')  # binary - vector
+os.makedirs(result_path, exist_ok=True)
+
+train_file = os.path.join(path_binary_vector, 'public', 'training_vectors.txt')
+
+train = LabelledCollection.load(train_file, load_binary_vectors)
+
+nF = train.instances.shape[1]
+
+print(f'number of classes: {len(train.classes_)}')
+print(f'number of training documents: {len(train)}')
+print(f'training prevalence: {F.strprev(train.prevalence())}')
+print(f'training matrix shape: {train.instances.shape}')
+
+dev_prev = pd.read_csv(os.path.join(path_binary_vector, 'public', 'dev_prevalences.csv'), index_col=0)
+print(dev_prev)
+
+
+
+
+scores = {}
+for quantifier in [CC]: #, ACC, PCC, PACC, EMQ, HDy]:
+
+    classifier = CalibratedClassifierCV(LogisticRegression())
+    model = quantifier(classifier).fit(train)
+    quantifier_name = model.__class__.__name__
+
+    scores[quantifier_name]={}
+    for sample_set, sample_size in [('dev', 1000)]:
+        ae_errors, rae_errors = [], []
+        for i, row in tqdm(dev_prev.iterrows(), total=len(dev_prev), desc=f'testing {quantifier_name} in {sample_set}'):
+            filename = row['filename']
+            prev_true = row[1:].values
+            sample_path = os.path.join(path_binary_vector, 'public', f'{sample_set}_vectors', filename)
+            sample, _ = load_binary_vectors(sample_path, nF)
+            qp.environ['SAMPLE_SIZE'] = sample.shape[0]
+            prev_estim = model.quantify(sample)
+            # prev_true  = sample.prevalence()
+            ae_errors.append(qp.error.mae(prev_true, prev_estim))
+            rae_errors.append(qp.error.mrae(prev_true, prev_estim))
+
+        ae_errors = np.asarray(ae_errors)
+        rae_errors = np.asarray(rae_errors)
+
+        mae = ae_errors.mean()
+        mrae = rae_errors.mean()
+        scores[quantifier_name][sample_set] = {'mae': mae, 'mrae': mrae}
+        pickle.dump(ae_errors, open(os.path.join(result_path, f'{quantifier_name}.{sample_set}.ae.pickle'), 'wb'), pickle.HIGHEST_PROTOCOL)
+        pickle.dump(rae_errors, open(os.path.join(result_path, f'{quantifier_name}.{sample_set}.rae.pickle'), 'wb'), pickle.HIGHEST_PROTOCOL)
+        print(f'{quantifier_name} {sample_set} MAE={mae:.4f}')
+        print(f'{quantifier_name} {sample_set} MRAE={mrae:.4f}')
+
+for model in scores:
+    for sample_set in ['validation']:#, 'test']:
+        print(f'{model}\t{scores[model][sample_set]["mae"]:.4f}\t{scores[model][sample_set]["mrae"]:.4f}')
+
+
+"""
+test:
+CC	0.1859	1.5406
+ACC	0.0453	0.2840
+PCC	0.1793	1.7187
+PACC	0.0287	0.1494
+EMQ	0.0225	0.1020
+HDy	0.0631	0.2307
+
+validation
+CC	0.1862	1.9587
+ACC	0.0394	0.2669
+PCC	0.1789	2.1383
+PACC	0.0354	0.1587
+EMQ	0.0224	0.0960
+HDy	0.0467	0.2121
+"""
+
+

LeQua2022/main_multiclass.py

@@ -8,6 +8,7 @@ from tqdm import tqdm
 import quapy as qp
 from quapy.data import LabelledCollection
 from quapy.method.aggregative import *
+from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as MLPE
 from data import load_multiclass_raw_document
 import os
 
@@ -30,10 +31,10 @@ train.instances = tfidf.fit_transform(train.instances)
 print(train.instances.shape[1])
 
 scores = {}
-for quantifier in [CC, ACC, PCC, PACC, EMQ]:#, HDy]:
-    classifier = CalibratedClassifierCV(LogisticRegression())
-    # classifier = LogisticRegression()
-    model = quantifier(classifier).fit(train)
+for quantifier in [MLPE()]:#[CC, ACC, PCC, PACC, EMQ]:#, HDy]:
+    # classifier = CalibratedClassifierCV(LogisticRegression())
+    # model = quantifier(classifier).fit(train)
+    model = quantifier.fit(train)
     print('model trained')
 
     quantifier_name = model.__class__.__name__
@@ -67,11 +68,20 @@ for model in scores:
 
 
 """
-test:
 
+MLPE	validation	0.0423	4.8582
+CC	validation	0.0308	2.9731
+PCC	validation	0.0296	3.3926
+ACC	validation	0.0328	3.1461
+PACC	validation	0.0176	1.6449
+EMQ	validation	0.0207	1.6960
 
-validation
-
+MLPE	test	0.0423	4.6083
+CC	test	0.0308	2.9037
+PCC	test	0.0296	3.2764
+ACC	test	0.0328	3.0674
+PACC	test	0.0174	1.5892
+EMQ	test	0.0207	1.6059
 """
 
 

quapy/data/base.py

@@ -35,7 +35,7 @@ class LabelledCollection:
             self.classes_ = np.unique(np.asarray(classes_))
             self.classes_.sort()
             if len(set(self.labels).difference(set(classes_))) > 0:
-                raise ValueError('labels contains values not included in classes_')
+                raise ValueError(f'labels ({set(self.labels)}) contain values not included in classes_ ({set(classes_)})')
         self.index = {class_: np.arange(n_docs)[self.labels == class_] for class_ in self.classes_}
 
     @classmethod

quapy/method/non_aggregative.py

@@ -10,6 +10,7 @@ class MaximumLikelihoodPrevalenceEstimation(BaseQuantifier):
     def fit(self, data: LabelledCollection, *args):
         self._classes_ = data.classes_
         self.estimated_prevalence = data.prevalence()
+        return self
 
     def quantify(self, documents, *args):
         return self.estimated_prevalence